Designing paper‐based records to improve the quality of nursing documentation in hospitals: A scoping review
Bibliographic record
Abstract
BACKGROUND: Inpatient nursing documentation facilitates multi-disciplinary team care and tracking of patient progress. In both high- and low- and middle-income settings, it is largely paper-based and may be used as a template for electronic medical records. However, there is limited evidence on how they have been developed. OBJECTIVE: To synthesise evidence on how paper-based nursing records have been developed and implemented in inpatient settings to support documentation of nursing care. DESIGN: A scoping review guided by the Arksey and O'Malley framework and reported using PRISMA-ScR guidelines. ELIGIBILITY CRITERIA: We included studies that described the process of designing paper-based inpatient records and excluded those focussing on electronic records. Included studies were published in English up to October 2019. SOURCES OF EVIDENCE: PubMed, CINAHL, Web of Science and Cochrane supplemented by free-text searches on Google Scholar and snowballing the reference sections of included papers. RESULTS: 12 studies met the eligibility criteria. We extracted data on study characteristics, the development process and outcomes related to documentation of inpatient care. Studies reviewed followed a process of problem identification, literature review, chart (re)design, piloting, implementation and evaluation but varied in their execution of each step. All studies except one reported a positive change in inpatient documentation or the adoption of charts amid various challenges. CONCLUSIONS: The approaches used seemed to work for each of the studies but could be strengthened by following a systematic process. Human-centred Design provides a clear process that prioritises the healthcare professional's needs and their context to deliver a usable product. Problems with the chart could be addressed during the design phase rather than during implementation, thereby promoting chart ownership and uptake since users are involved throughout the design. This will translate to better documentation of inpatient care thus facilitating better patient tracking, improved team communication and better patient outcomes. RELEVANCE TO CLINICAL PRACTICE: Paper-based charts should be designed in a systematic and clear process that considers patient's and healthcare professional's needs contributing to improved uptake of charts and therefore better documentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.192 | 0.435 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.039 | 0.035 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".